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\section{probability distribution}  The end result of this project will be a series of volumes sampled from an unknown probability distribution. We want to determine what that distibution will be. We will have a functional form for that distribution $P(V | \theta )$ where $\theta$ are the unknown parameters of the distibution. In the past we've used a generalized Gaussian. $P(V | \theta)$ is the biased distribution, so it will be the generalized gaussian times $V$. This is mostly taken from \url{http://en.wikipedia.org/wiki/Distribution_fitting}  There are several ways to determine what the values of $\theta$ should be. None of which involve histogramming.  \subsection{maximum likelihood method}  \url{http://en.wikipedia.org/wiki/Maximum_likelihood}  The likelihood is the joint probability distribution of all the observations given the set of parameters $\theta$  \begin{equation}  L(V_1, ..., V_N | \theta) = \prod_{i=1}^{N} P(V_i | \theta) 
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\subsection{Bayesian updates}  \url{http://en.wikipedia.org/wiki/Bayesian_inference#Parametric_formulation}  With this method you start with some prior distribution over the parameters $P(\theta)$ and apply Bayes' theorem iteratively to update the distribution given the additional knowledge (the fact of the observation).  \begin{equation}  P(\theta | V_1) = \frac{ P(V_1 | \theta) P(\theta) }{ P(V_1)}